The proposed network was trained with a set of 2007 normal lung CT slices and 870 lesioned slices which were randomly sampled from the COVID-cell dataset [1]}, and then evaluated on two test sets of lesioned images. One test set includes 128 lesioned images where were randomly sampled from the COVID-cell dataset and then annotated at pixel level by one logists. Note that there is no overlap between the training set and this test set although they are both from the COVID-cell dataset.
The other test set consist of 493 lesioned slices from the COVID-19 Image Data Collection [2]} which contains 20 cases with covid-19. This dataset was released with lesion area annotations, although the pixel-level annotations are not that accurate particularly around the boundary of the lesion area.
It is worth noting that all the pixel-level lesion annotations were not for model training but only for quantitative evaluation of the proposed model.
